摘要
      抗生素和抗生素抗性基因（ARGs）在水生环境中经常被检测到，并被视为新出现的污染物。基于反向传播神经网络（BPNN），通过训练输入和输出，构建了膜分离技术对四种目标抗生素去除效果的预测模型。抗生素的膜分离试验表明，微滤对阿奇霉素和环丙沙星的去除效果较好，基本在80%以上。超滤和纳滤对磺胺甲恶唑（SMZ）和四环素（TC）的去除效果较好。渗透物中SMZ和TC的浓度之间存在很强的相关性，训练和验证过程的R2超过0.9。输入层变量与预测目标之间的相关性越强，BPNN模型的预测性能就越好。这些结果表明，所建立的BPNN预测模型能够更好地模拟膜分离技术对目标抗生素的去除。该模型可用于预测和探索外部条件对膜分离技术的影响，为BPNN模型在环境保护中的应用提供一定的依据。
Abstract
Antibiotics and antibiotic resistance genes (ARGs) have been frequently detected in the aquatic environment and are regarded as emerging pollutants. The prediction models for the removal effect of four target antibiotics by membrane separation technology were constructed based on back propagation neural network (BPNN) through training the input and output. The membrane separation tests of antibiotics showed that the removal effect of microfiltration on azithromycin and ciprofloxacin was better, basically above 80%. For sulfamethoxazole (SMZ) and tetracycline (TC), ultrafiltration and nanofiltration had better removal effects. There was a strong correlation between the concentrations of SMZ and TC in the permeate, and the R2 of the training and validation processes exceeded 0.9. The stronger the correlation between the input layer variables and the prediction target was, the better the prediction performances of the BPNN model than the nonlinear model and the unscented Kalman filter model were. These results showed that the established BPNN prediction model could better simulate the removal of target antibiotics by membrane separation technology. The model could be used to predict and explore the influence of external conditions on membrane separation technology and provide a certain basis for the application of the BPNN model in environmental protection.

https://www.tandfonline.com/doi/abs/10.1080/10934529.2023.2200719